As is known in the art, machine learning techniques may be utilized to make a prediction with respect to given data. Generally, a classification algorithm is a method in which training dataset is provided to the algorithm and utilized for learning. After a training phase, the classification algorithm may be adapted to the specific problem at hand and may be able to predict information with respect to new, optionally unseen, instances.
The training data may comprise a sample of data-points, each given using a set of features which are used by the classifier, as well as the label, which is to be predicted. As an example, in case a gender of a person is predicted, the features may be height, age, weight and first name. It will be noted that in some cases, some features are useful for the prediction while others may not be useful. In this example, in addition to the features, a gender label is given for each data-point, so as to enable the classifier to learn how to predict such information for new data-points.
After the training phase is concluded, features of data-points are given and the classifier may determine a predicted label. In some cases, feedback may be given to indicate to the classifier whether the prediction was correct.